POS1168 NOMOGRAM FOR IDENTIFYING THE HIGH-RISK PATIENTS OF ORGAN INVOLVEMENT IN EARLY-STAGE ANTI-NEUTROPHIL CYTOPLASMIC ANTIBODY-ASSOCIATED VASCULITIS
Authors: R. Wu, R. Su, C. Wang
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is characterized by small-vessel vasculitis and systemic autoimmune inflammatory response [1, 2]. Although AAV is rare, the condition of AAV is relatively severe. The clinical manifestations of patients with AAV are different [3], ranging from mild non-specific symptoms to severe life-threatening organ involvement. Therefore, the clinical need to achieve accurate and quick identification for the high-risk groups of organ involvement in early-stage AAV remains strong. <h3>Objectives</h3> There is no study to identify the high-risk patients with organ involvement of early-stage of AAV. We aimed to develop a model to identify those high-risk patients at an early stage. <h3>Methods</h3> The data of the 74 new-onset patients with AAV (61 of whom had definite organ involvement) was collected in our retrospective study. The predictor variables for organ involvement in AAV were assessed by the logistic regression analysis. We developed the risk stratification model by nomogram. And the model was validated by concordance index, calibration curve, and decision curve analysis. <h3>Results</h3> The logistic regression analysis showed that BVAS, IL-17 and Treg cells were the predictor variables for organ involvement in AAV (Figure 1). We constructed a nomogram model consisting of BVAS, Treg cells and IL-17 (Figure 2A). In the nomogram, the value of each predictor was assigned a certain number of points, and the sum of the points for each predictor corresponded to the risk of organ involvement in AAV. Then we evaluated the nomogram model. The calibration curve in 1000 bootstrap replications revealed a good predictive accuracy between the predicted probability and observed probability of the nomogram model with a concordance index (C-index) of 0. 957 (95% CI = 0. 694–0.960) and bias-corrected C-index of 0. 937 (Figure 2B). And the chi-squares of Hosmer–Lemeshow test was 0.37(<i>P</i> = 0.83) indicating a good fitness of the model. Next, the receiver operating characteristic (ROC) curve of the model (Figure 2C) yielded the AUC of the curve was 0.9571 (95%CI =0.9124–1.000) showing it had a great ability for discriminating organ involvement in AAV. The decision curve analysis (DCA) showed a positive net benefit for patients with organ involvement (Figure 2D), which suggested that patients with organ involvement could benefit from interventions. And the clinical impact curve (Figure 2E) showed that the risk of organ involvement predicted by the nomogram model under threshold probability (red curve) was close to the actual value of organ involvement events under each threshold probability (blue curve). <h3>Conclusion</h3> The risk stratification nomogram model was effective to identify the high-risk patients of organ involvement in early-stage AAV, and it was validated with good discrimination, calibration as well as great application value in clinical. <h3>References</h3> [1]Kitching AR, Anders H-J, Basu N, Brouwer E, Gordon J, Jayne DR et al. ANCA-associated vasculitis. Nat Rev Dis Primers. 2020, 6(1):71. [2]Nakazawa D, Masuda S, Tomaru U, Ishizu A. Pathogenesis and therapeutic interventions for ANCA-associated vasculitis. Nat Rev Rheumatol. 2019, 15(2). [3]Yaseen K, Mandell BF. ANCA associated vasculitis (AAV): a review for internists. Postgrad Med. 2022. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.